AI Landing Zone on AWS | Secure AI Platform for Agentic AI

Several Clouds helps organizations move from AI experiments to production-ready AI platforms on AWS. We design and implement a secure, automated, multi-account AWS landing zone purpose-built for generative AI, Amazon Bedrock, RAG applications, AI agents, and regulated AI workloads.

The result is an AWS foundation where every model call, data flow, workload account, and deployment path is secure, observable, compliant, and cost-aware before it reaches production. Your team builds the AI features; we build the platform underneath.

What you get

• A production-ready, multi-account AWS landing zone built on AWS Control Tower: account structure, networking, identity, centralized logging, monitoring, security guardrails, and environment separation.
• AI platform governance for Amazon Bedrock, AgentCore, RAG, and agentic workloads: approved access patterns, guardrails, audit trails, model invocation logging, cost attribution, and observability.
• Golden paths for AI teams: reference architectures, Infrastructure as Code modules, CI/CD templates, security checks, and deployment patterns for common AI workloads such as chat, summarization, RAG, copilots, and AI agents.
• Security and compliance by design, aligned to frameworks such as SOC 2, ISO 27001, GDPR, PCI DSS, and HITRUST. Several Clouds is ISO 27001 certified by Bureau Veritas.
• FinOps built into the architecture: model selection trade-offs, token usage visibility, budget alerts, cost anomaly detection, and workload-level cost attribution.
• Cloud-native implementation using AWS managed and serverless services where appropriate, including Amazon Bedrock, Lambda, Fargate, RDS, DynamoDB, OpenSearch, and related AWS services.

Who it is for

Organizations building generative AI, RAG, copilots, AI agents, or AI-native products on AWS. This offering is especially relevant for regulated industries such as financial services, healthcare, public sector, and enterprise environments that need secure, auditable AI adoption, as well as AI-native startups that need to scale from prototype to production without rebuilding their AWS foundation later.

Outcomes

• A secure, compliant, production-grade AWS environment ready for AI and agentic workloads.
• Faster path from AI prototype to production through reusable architecture patterns, IaC modules, CI/CD templates, and platform guardrails.
• Improved visibility into model usage, token consumption, latency, errors, guardrail violations, and workload-level cost.
• Lower cloud run-rate through cost-aware architecture and FinOps practices, with typical optimization opportunities in the 10-30% range where cost reduction applies.
  • Internal capability, not long-term dependency: onboarding, architecture knowledge transfer, and training included where relevant.